Deep Joint Transmission-Recognition for Power-Constrained IoT Devices
Year: 2020
Type: preprint
Abstract: We propose a joint transmission-recognition scheme for efficient inference at the wireless network edge. Our scheme allows for reliable image recognition over wireless channels with significant computational load reduction at the sender side. We incorporate recently proposed deep joint source-channel coding (JSCC) scheme, and combine it with novel filter pruning strategies aimed at reducing the redundant complexity from neural networks. We evaluate our approach on a classification task, and show satisfactory results in both transmission reliability and workload reduction. This is the first work that combines deep JSCC with network pruning and applies it to images classification over wireless network.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3010264432